文献类型：期刊文献

英文题名：A probabilistic approach to assessing project complexity dynamics under uncertainty

作者：Luo, Lan[1];Zhang, Limao[2];Yang, Delei[3];He, Qinghua[4]

第一作者：Luo, Lan

通讯作者：Zhang, LM[1]

机构：[1]Nanchang Univ, Sch Civil Engn & Architecture, Nanchang 330031, Jiangxi, Peoples R China;[2]Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore 639798, Singapore;[3]Henan Univ Econ & Law, Sch Construct Management & Real Estate, Zhengzhou 45000, Peoples R China;[4]Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China

第一机构：Nanchang Univ, Sch Civil Engn & Architecture, Nanchang 330031, Jiangxi, Peoples R China

通讯机构：[1]corresponding author), Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore 639798, Singapore.

年份：2022

卷号：26

期号：8

起止页码：3969-3985

外文期刊名：SOFT COMPUTING

收录：;EI(收录号：20214711195416);Scopus(收录号：2-s2.0-85119300914);WOS:【SCI-EXPANDED(收录号:WOS:000718094800006)】；

基金：This study is supported by the National Natural Science Foundation of China (72061025, 71901113, 71640012, 71801083, and 71971161), Social Science Foundation of Jiangxi Province (21GL05), and China Scholarship Council Project (201806825066).

语种：英文

外文关键词：Project complexity; Evolutionary dynamics; Bayesian network; Predictive analysis; Sensitivity analysis

摘要：Intractable complexity may be encountered as the construction project advances. Existing research rarely investigates the time-updated dynamic in project complexity as the project process progresses. This study develops a novel systematic soft computing approach based on Bayesian inference to explore the evolutionary dynamics in project complexity under uncertainty. By learning the network structure and parameters from given data, a dynamic Bayesian network model is established to simulate the complex interrelations among 7 complexity-related variables. The developed approach is capable of performing predictive, sensitivity, and diagnostic analysis on a quantitative basis. The construction project of EXPO 2010 is used to testify the effectiveness and applicability of the developed approach. Results indicate that (1) more attention should be paid to technological complexity and task complexity in the process of complexity management; (2) the developed dynamic Bayesian network approach can model the evolutionary dynamics of project complexity at different scenarios; and (3) the complexity level of a specific construction project over time can be predicted in a dynamic manner. This research contributes to (a) the state of the knowledge by proposing a systematic soft computing methodology that can model and identify the dynamic interactions of project complexity factors over time, and (b) the state of the practice by gaining a better understanding of the most sensitive factors for managing complexity in a changing project environment.